Workforce Scheduling Algorithm for Flexible Staffing

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Solution Overview

Problem

Conventional workforce scheduling methods require a trial-and-error approach to determine the number of flexible workers needed, which is cumbersome and time-consuming, as they involve running multiple scenarios to compare staffing plans for enterprises with fixed and flexible workers.

Innovation Solution

The system automatically schedules a workforce by receiving shift constraints and generating shift templates, determining the number of shift instances to cover forecasted demand, and producing staff mix enumerations, using dynamic programming to assign shifts to workers while adhering to their flexibility classifications, prioritizing non-flexible workers before flexible ones.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a trial-and-error approach is used to determine the number of flexible workers, then the staffing plan can be generated, but the process becomes cumbersome and time-consuming

Engineering Contradiction:

Improvestaffing plan generation speedVSAvoidtime required for scenario testing

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual trial-and-error mechanical process with an automated computer-based scheduling system that uses algorithms to instantly calculate optimal staff mixes, eliminating the need for manual scenario testing and significantly reducing time consumption

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system automatically varies multiple parameters (number of flexible workers, staff mix compositions, scheduling scenarios) simultaneously through computational algorithms, allowing rapid exploration of different staffing configurations without manual intervention

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If multiple scenarios are run to compare staffing plans, then the optimal staffing plan can be identified, but the process becomes complex and labor-intensive

Engineering Contradiction:

Improvestaffing plan optimization accuracyVSAvoidscheduling process complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The scheduling system is designed to handle multiple scenarios and comparison functions within a single integrated platform, allowing users to input different parameters and receive optimized staffing plans without needing separate analysis tools or manual comparison processes

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system performs self-optimization by automatically evaluating different staffing configurations and identifying optimal solutions based on predefined criteria, eliminating the need for manual scenario analysis and comparison by the user

Inventive Principle:

Principle #25Self-service

3Productivity

If dynamic programming is used to assign shifts, then the optimal worker classification mix can be determined, but the computational requirements increase

Engineering Contradiction:

Improveshift assignment efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The complex shift assignment problem is segmented into smaller sub-problems by classifying workers into different categories (flexible and non-flexible) and assigning shifts in staged manner, which reduces the computational complexity of the overall dynamic programming optimization

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS8666795B2Systems and methods of automatically scheduling a workforce

Publication Date: 2014.03.04 VERINT SYST INC

AI summary

Systems and methods of workforce scheduling are disclosed. One example embodiment, among others, comprises a computer-implemented method of scheduling workers. Each worker is associated with one of a set of flexibility classifications, which include non-flex-time and at least one flex-time. The method includes generating a set of shift instances to cover forecasted demand over a planning period, and assigning the shift instances to the set of workers by iterating through the each of the workers to assign at least a portion of the shift instances to a selected one of the workers. The assigning is such that total hours assigned to the selected worker depends on a number associated with the classification of the selected worker.